L’ojectif est de savoir si avec ceratines fonction de imager, est possible de compter les cellules

# load image
papSmear <- load.image('papSmear.jpg')
papSmear
## Image. Width: 493 pix Height: 335 pix Depth: 1 Colour channels: 3
par(mfrow=c(1,2))
plot(papSmear)
plot(grayscale(papSmear))

Depth = 1 means the object is a static image and not video.
Clour channels = 3 means the image content Red, Green, Blue colours.

layout(t(1:2))
hist(papSmear)
hist(grayscale(papSmear))

hist function uses the values of the 3 channels rgb as a vector values. We can plot separately histograms by color

# for red
G(papSmear) %>% hist(main="Green channels values")

If we convert the image object’s to dataframe, we can view the value of each pixel.

layout(t(1:2))
head(as.data.frame(papSmear))
tail(as.data.frame(papSmear))
library(ggplot2)
papSeam_df <- as.data.frame(papSmear)
pap <- plyr::mutate(papSeam_df,channel=factor(cc,labels=c('R','G','B')))
ggplot(pap,aes(value,col=channel)) + geom_histogram(bins=30) + facet_wrap(~ channel)

papSmearg <- grayscale(papSmear)
f <- ecdf(papSmearg)
plot(f, main="Empirical CDF of luminance values (gray)")

gr <- imgradient(papSmear,"xy")
grG <- imgradient(grayscale(papSmear), "xy")

plot(gr, layout="row")

plot(grG, layout="row")

layout(t(1:2))
dx <- imgradient(papSmear,"x")
dy <- imgradient(papSmear,"y")
grad.mag <- sqrt(dx^2+dy^2)
plot(grad.mag,main="Gradient magnitude with color")

papSmear.G <- grayscale(papSmear)
dx.G <- imgradient(papSmear.G,"x")
dy.G <- imgradient(papSmear.G,"y")
grad.mag.G <- sqrt(dx.G^2+dy.G^2)
plot(grad.mag.G ,main="Gradient magnitude with gray")

l <- imgradient(papSmear,"x")
head(as.data.frame(l))
dim(l)
## [1] 493 335   1   3
par(mfrow=c(1,2))
plot(imhessian(papSmear)[2])

plot(with(imhessian(papSmear),(xx*yy - xy^2)), main="Determinant of Hessian")

layout(t(1:2))
threshold(papSmear,"99%") %>% plot(main="Determinant: 1% highest values (raw) ")
threshold(with(imhessian(papSmear),(xx*yy - xy^2)),"99%") %>% plot(main="Determinant: 1% highest values")

threshold(grad.mag, "99%") %>% plot(main="Determinant: 1% highest values (gradient)")

lab1 <- threshold(grad.mag, "99%") %>% label
lab2 <- threshold(with(imhessian(papSmear),(xx*yy - xy^2)), "99%") %>% label
par(mfrow=c(1,2))
plot(lab1, main= "Labelled regions from gradient")
plot(lab2, main= "Labelled regions from scare")

class(lab1)
## [1] "cimg"         "imager_array" "numeric"
lab1
## Image. Width: 493 pix Height: 335 pix Depth: 1 Colour channels: 3
df1 <- as.data.frame(lab1) %>% subset(value>0)
df2 <- as.data.frame(lab2) %>% subset(value>0)
head(df2,3)
par(mfrow=c(1,2))
centers1 <- dplyr::group_by(df1,value) %>% dplyr::summarise(mx=mean(x),my=mean(y))
plot(lab1)
with(centers1, points(mx, my, col="yellow"))

centers2 <- dplyr::group_by(df2,value) %>% dplyr::summarise(mx=mean(x),my=mean(y))
plot(lab2)
with(centers2, points(mx, my, col="red"))

get.centers <- function(im,thr="99%")
{   im <- isoblur(im,5)
    dt <- imhessian(im) %$% { xx*yy - xy^2 } %>% threshold(thr) %>% label
    as.data.frame(dt) %>% subset(value>0) %>% dplyr::group_by(value) %>% dplyr::summarise(mx=mean(x),my=mean(y))
}
par(mfrow=c(1,2))
plot(lab1, main ="")
get.centers(lab1,"50%") %$% points(mx,my,col="yellow")
plot(grad.mag,main="Gradient magnitude with color")

Conclusion

Cette méthode ne permet pas de compter les cellules.

Faire des essays avec Pixsets

im <- load.image("papSmear.jpg")
im <- grayscale(im)
px <- im > .6
par(mfrow=c(1,2))
plot(im, main = "raw image")
plot(px, main = ">0.6 intensity")

dim(px)
## [1] 493 335   1   1
dim(im)
## [1] 493 335   1   1
mean(px)
## [1] 0.8716418
mean(im)
## [1] 0.7699147
mean(im[px])
## [1] 0.8081526
par(mfrow=c(1,2))
plot(im , main = "im")
#highlight(im)
px <- isoblur(im, 1)  > .6
plot(px, main ="px")
highlight(px)

par(mfrow=c(1,2))
plot(im)
px.flood(im,278,300,sigma=.31) %>% highlight
sp <- split_connected(px)
plot(sp[1:4])

par(mfrow=c(1,2))
boundary(px) %>% plot(main = "px <- isoblur(im, 1)  > .6")
boundary(im) %>% plot( main= "im")

par(mfrow=c(1,2))
plot(im, main= "im with boundary")
boundary(px) %>% where %$% { points(x,y,cex=.1,col="red") }
plot(im, main="im with highlight(px)")
highlight(px)

################
#par(mfrow=c(1,2))
plot(im, main="with shink")
highlight(px)
#Shrink by 5 pixels
shrink(px,5) %>% highlight(col="blue")
#Grow by 5 pixels
grow(px,5) %>% highlight(col="green")

#Compute bounding box
bbox(px) %>% highlight(col="yellow")
## Warning in contourLines(1:width(x), 1:height(x), ., nlevels = 2, levels =
## c(0, : all z values are equal
px.none(im) #No pixels
## Pixel set of size 0. Width: 493 pix Height: 335 pix Depth: 1 Colour channels: 1
px.all(im) #All of them
## Pixel set of size 165155. Width: 493 pix Height: 335 pix Depth: 1 Colour channels: 1
plot(im)
#Image borders at depth 10
px.borders(im,10) %>% highlight
#Left-hand border (5 pixels), see also px.top, px.bottom, etc.
px.left(im,5) %>% highlight(col="green")

#Split pixset in two along x
imsplit(im,"x",2) %>% plot(layout="row")

plot(im > .6)

# Splitting by color
imc <- load.image("papSmear.jpg")
imsplit(imc,"c") %>% plot

plot(imc)
imsplit(imc > 0.75,"c") %>% parany %>% highlight

threshold(imc) %>% plot

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
d <- as.data.frame(im)
##Subsamble, fit a linear model
m <- sample_n(d,1e4) %>% lm(value ~ x*y,data=.)
##Correct by removing the trend
im.c <- im-predict(m,d)
out <- threshold(im.c)
plot(out)

out <- clean(out,3) %>% imager::fill(7)
plot(im)
highlight(out)

bg <- (!threshold(im.c,"10%"))
fg <- (threshold(im.c,"90%"))
imlist(fg,bg) %>% plot(layout="row")